mlsanityData to 7 Oct 2026

Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems

arXiv 2306.13686 ↗First posted 22 Jun 2023cs.CY cs.AI cs.HCcited by 2

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Abstract (arXiv)

The increased use of AI systems is associated with multi-faceted societal, environmental, and economic consequences. These include non-transparent decision-making processes, discrimination, increasing inequalities, rising energy consumption and greenhouse gas emissions in AI model development and application, and an increasing concentration of economic power. By considering the multi-dimensionality of sustainability, this paper takes steps towards substantiating the call for an overarching perspective on "sustainable AI". It presents the SCAIS Framework (Sustainability Criteria and Indicators for Artificial Intelligence Systems) which contains a set 19 sustainability criteria for sustainable AI and 67 indicators that is based on the results of a critical review and expert workshops. This interdisciplinary approach contributes a unique holistic perspective to facilitate and structure the discourse on sustainable AI. Further, it provides a concrete framework that lays the foundation for developing standards and tools to support the conscious development and application of AI systems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 84 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

24 of 84 references.

References · 24

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12 more
  1. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  2. 2026A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasmscited by 0